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1 Systems and Synthetic Biology: A programming languages point of view Saurabh Srivastava Assistant Research Engineer Computer Science + Bioengineering.

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Presentation on theme: "1 Systems and Synthetic Biology: A programming languages point of view Saurabh Srivastava Assistant Research Engineer Computer Science + Bioengineering."— Presentation transcript:

1 1 Systems and Synthetic Biology: A programming languages point of view Saurabh Srivastava Assistant Research Engineer Computer Science + Bioengineering University of California, Berkeley

2 Systems biology – Modeling complete biological processes Genomics – Reading DNA sequences got incredibly fast, and cheap – Algorithms for sequence data analytics within organisms Synthetic biology – DNA synthesis got incredibly cheap – Functional characterization on the way – Algorithms to predict tweaks to organisms 2 One view: biological specifications One view: syntax of components One view: semantics of components In perspective

3 3 Different scales in biology Systems Biology operates here Synthetic Biology operates here Intracellular processes Intercellular processes Protein interactions Protein function Organisms

4 4 Systems Biology operates here Results and techniques used Synthetic Biology operates here Intracellular processes Intercellular processes Protein interactions Protein function

5 5 Systems Biology operates here Results and techniques used Synthetic Biology operates here Intracellular processes Intercellular processes Protein interactions Protein function Results: Constructed a bacteria that produces Paracetamol/Depon Model of cell communication allows understanding cancer Techniques: Automatically generating concurrent programs using input-output examples Big data analysis and abstraction

6 PART I Synthesizing models for Systems Biology

7 Synthesizing Systems Biology “Programs” Synthesizing concurrent programs from examples Programs ≡ biological models Examples ≡ biological experiments We assist natural sciences with formal methods Given experiments, are there other models? If so, compute a new, disambiguating experiment Part I: how stem cells coordinate their fates 7

8 Understanding Diseases “Cancer is fundamentally a disease of failure of regulation of tissue growth. In order for a normal cell to transform into a cancer cell, the genes which regulate cell growth and differentiation must be altered.” – from Wikipedia Research on cell differentiation helps understanding diseases such as cancer. 8

9 C. elegans: A Model Organism Earthworm used in developmental biology. 959 cells; its organs found in other animals. Differentiation studied on vulval development. 9

10 Differentiation and then development into organ parts 10 Initial division of embryo Identical precursor cells collaborate to decide their fate

11 Modeling Goal 11 What is the mechanism (program) within each cell for deciding fates through communication?

12 Building Blocks of these Programs Cells contain communicating proteins. Protein interaction: a protein senses the concentration of other proteins. Interaction is either activation or inhibition. 12 A B A B

13 How the Vulval Cells Differentiate 13 let-23 lin-12 sem-5 let-60 mpk-1 lst let-23 lin-12 sem-5 let-60 mpk-1 lst let-23 lin-12 sem-5 let-60 mpk-1 lst Anchor Cell Anchor Cell high med low 1º 2º 3º If cells sense the same signal strength, data races occur....

14 How Biologists Discover Interactions Measuring protein levels over time is infeasible If such “cell tracing” is infeasible, infer protein interaction from end-to-end experiments That is, mutate cells  observe resulting fates 14

15 A Mutation Experiment 15 let-23 lin-12 let-23 lin-12 let-23 lin-12 Anchor Cell Anchor Cell high med low 1º 2º 3º 1º...

16 Putting Experiments Together 16 ExperimentAClin-12lin-15let-23lstFate decisions 1ON {332123} 2ONOFFON {331113} 3ON OFFON {112121,122121, } No protein is mutated. lin-12 is turned off. Multiple outcomes observed Fate of six neighboring cells Experiments over 35 years by 11 groups

17 How to Build Accurate Models? 17 let-23 lin-12 sem-5 let-60 mpk-1 lst let-23 lin-12 sem-5 let-60 mpk-1 lst let-23 lin-12 sem-5 let-60 mpk-1 lst Anchor Cell Anchor Cell high med low 1º 2º 3º...

18 Semantics of the Modeling Language 18 Cell 1Cell 2 Program has cells Non-deterministic outcomes via schedule interleaving let-23 lin-12 sem-5 let-60 mpk-1 lst Cell has proteins All proteins advance synchronously Proteins have discrete state and update functions.

19 Synthesizing Cellular Programs 19

20 Synthesis of Programs 20 specification biological insight biological insight synthesizer completed program completed program ExperimentAClin-12lin-15let-23lstFate decisions 1ON {332123} 2ONOFFON {331113} 3ON OFFON {112121,122121, }... Given as a partial program

21 Partial Programs Partial programs express biological insight: Which proteins are in the cell Which proteins may interact Update functions can be unknown. 21 let-23 lin-12 sem-5 let-60 mpk-1 lst ?

22 Synthesis Algorithm 22

23 Classical CEGIS synthesizer initial input/output set candidate solution SAT add input-output counterexample SAT UNSAT 23 verifier

24 Correctness Condition Safety: all schedules must lead the program to produce experiment outcomes observed in the wet lab. ∀ mutation m. ∀ schedule s. P(m, s) ∈ E(m) Completeness: each observed experiment outcome must be reproducible by the program for some schedule. ∀ mutation m. ∀ fate f ∈ E(m). ∃ schedule s. P(m, s) = f 24 ExperimentAClin-12lin-15let-23lstFate decisions 1ON {332123} 2ONOFFON {331113} 3ON OFFON {112121, , }...

25 Counterexample-Guided Inductive Synthesis 25 inductive synthesizer inductive synthesizer safety verifier safety verifier counterexample: execution P(m, s) with bad outcome counterexample: observation (m, f) not reproducible completeness verifier completeness verifier candidate completion initial set of input/output examples no candidate completion ok

26 Verifying for Safety Safety: ∀ mutation m. ∀ schedule s. P(m, s) ∈ E(m) Attempt to disprove by searching for a demonic schedule: ∃ mutation m. ∃ schedule s. P(m, s) ∉ E(m) 26 Unroll over the set of performed experiments Search symbolically for a demonic schedule

27 Verifying for Completeness Completeness: ∀ mutation m. ∀ fate f ∈ E(m). ∃ schedule s. P(m, s) = f Attempt to disprove by showing lack of an angelic schedule for some outcome: ∃ mutation m. ∃ fate f ∈ E(m). ∀ schedule s. P(m, s) ≠ f 27 Unroll over pairs of mutation and fate Query symbolically for an angelic schedule

28 Synthesized Models We synthesized two models of VPCs. Input: Partial model that specifies known, simple protein behaviors. Output: Synthesized update functions for two key proteins. 28 model 1 model 2

29 Additional Algorithms for Going Beyond Synthesis to Assist Scientists 29 Does there exist alternative model that differs on new experiment? For two or more models within the space, do there exist disambiguating experiments? What are the minimal number of experiments that constrain the space to current model?

30 PART II Predicting DNA insertions for Synthetic Biology

31 Microbial chemical factories – Sustainable bacterial production of chemicals. E.g., drugs, polymers Bacteria as a sensor – Agricultural apps: e.g., sense nitrogen depletion in soil, change color Tumor killing bacteria – Sense multiple environmental factors (lower oxygen, high lactic acid) – Invade cancerous cell – Release drug inside cell 31 Synthetic Biology inserts DNA Applications enabled

32 Does it actually work? Jay D. Keasling – Artemisinin: from Artemisia annua to Yeast – Amyris company: 219M incidence, 300M cure target – 8 years of work; manual insights Computationally predicted – Our tylenol E. coli strain Sugar Tyenol

33 Incoming chemicals How do cells manipulate chemicals Some proteins are enzymes Output chemicals

34 Changing the cellular chemical machinery What happens when we add unnatural/external enzymes?

35 35 Sugar Acetaminophen Tylenol Bio repositories Data dedup/correlation + Search DNA synthesis + Plasmid Transformation Abstractions or rules from data Polymers Nylon etc. Biofuels Sugar Opportunity Current status + Rule application to predict

36 36 Tylenol4-aminophenol4-aminobenzoate Chorismate pathway 4ABH gene from Mushroom Prediction for Tylenol

37 Tylenol 4AP 4ABH gene inserted

38 38 Abstractions or rules from data Polymers Nylon etc. Biofuels Sugar Opportunity Rule application to predict

39 Biochemical rules/abstractions?

40 f Graph transformation function Operator taking (chemical) graph and transforming it f’ f’’ f class ** **

41 Open problem 1: Language for chemicals and transforms C1=CC=CC=C1 3D2D1D XYZ, CML, PDB SMILES, SYBYL Good for crystallographers Good for biochemists Good for computational storage, retrieval

42 Alternative new representation Transformation representation – enc molecule – Be able to efficiently compute f class (enc molecule ) Conflicting objectives: – Trace-based encoding Difficulty at cycle cuts – True graph encoding Subgraph isomorphism Need midway encoding ** **

43 Applying reaction operators We can now predict new edges: I.e., external enzymes or even new constructed enzymes

44 Open problem 2: Deriving biochemical programs f class (enc molecule ) is a single step Function composition to get “pathway programs” – f class0 (f class1 (f class2 (f class3 (enc molecule )))) Complications – Path can be “acyclic” not just straight-line – Mixed concrete and rule instantiation

45 Crux of the research problem Intelligent rule instantiation: edges Given target chemical Phrase it as a model checking reachability problem: initial experiments point to a need for more efficient representation of search space. Need better search methods

46 Arbekacin (semi-synthetic) MRSA Drug $20,900/gram Amikacin (natural) Nothing interesting $118/gram Eventual targets

47 Arbekacin (semi-synthetic) MRSA Drug $20,900/gram Modular decomposition through function summaries Amikacin (natural) Nothing interesting $118/gram

48 Acknowledgements 48

49 49 Sugar Acetaminophen Tylenol Bio repositories Data dedup/correlation + Search DNA synthesis + Plasmid Transformation Chemical transformation functions from data Language for biochemistry Synthesis of biochemical program (i.e., DNA modifications) Synthesis of internals of transformation function Polymers Nylon etc. Biofuels Sugar Opportunity Current status +

50 Backup slides

51 51 Nylon precursor (polymer)

52 52 Butanol (fuel)

53 Architecture 55,000 entries Pubchem: 53M entries Uniprot: 23M entries Organisms: Pubchem names/Bren da names: 55k


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